Recently, many methods have been proposed for remote sensing image super-resolution. For example, a new unsupervised hourglass neural network is proposed in [1]} to super-resolved remote sensing images. The model uses a generative random noise to introduce a higher variety of spatial patterns, which can be promoted to a higher scale according to a global reconstruction constraint. In [2]}, a Deep Residual Squeeze and Excitation Network (DRSEN) is proposed to overcome the problem of the high complexity of remote sensing image distribution. In [3]}, a mixed high-order attention network (MHAN) is proposed, which consists of a feature extraction network for feature extraction and a feature refinement network with the high-order attention mechanism for detail restoration. In [4]}, the authors developed a Dense-Sampling Super-Resolution Network (DSSR) to explore the large-scale SR reconstruction of the remote sensing imageries.
